A Meta-Learning Approach to Methane Concentration Value Prediction

Conference paper

DOI: 10.1007/978-3-319-34099-9_56

Part of the Communications in Computer and Information Science book series (CCIS, volume 613)
Cite this paper as:
Kozielski M. (2016) A Meta-Learning Approach to Methane Concentration Value Prediction. In: Kozielski S., Mrozek D., Kasprowski P., Małysiak-Mrozek B., Kostrzewa D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS 2015, BDAS 2016. Communications in Computer and Information Science, vol 613. Springer, Cham

Abstract

A meta-learning approach to stream data analysis is presented in this work. The analysis is based on prediction of methane concentration in a coal mine. The results of the analysis show that the chosen approach achieves relatively low error values. Additionally, the impact of a data window size on a learning speed and quality was verified. The analysis is performed on a stream of measurements that was generated on a basis of real values collected in a coal mine.

Keywords

Meta-learning Algorithm selection Stream data analysis Prediction 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of ElectronicsSilesian University of TechnologyGliwicePoland

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